code: def extract_alpha_beta_for_device_mesh(
      alpha_beta_dict: Dict[Tuple[int], Tuple[float]],
      logical_mesh_shape: Tuple[int],
  ):
      """
      This method is used to extract the mesh_alpha and mesh_beta for the given logical_mesh_shape
      from the alpha_beta_dict. These two values will be used to estimate the communication cost.
      """
  
imports: from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  from torch.fx.graph import Graph
  from colossalai._analyzer.fx.codegen import ActivationCheckpointCodeGen
  from colossalai._analyzer.fx.graph_module import ColoGraphModule
  from colossalai._analyzer.fx.passes import shape_prop_pass
  from colossalai._analyzer.fx.tracer.tracer import ColoTracer
  from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
  from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
  from colossalai.auto_parallel.tensor_shard.options import DataloaderOption, ShardOption, SolverOptions, SolverPerference
  from colossalai.auto_parallel.tensor_shard.sharding_strategy import CommAction
  from colossalai.auto_parallel.tensor_shard.solver import CostGraph, Solver, StrategiesConstructor
  from colossalai.device.alpha_beta_profiler import AlphaBetaProfiler
  from colossalai.device.device_mesh import DeviceMesh
  from colossalai.tensor.sharding_spec import ShardingSpec
label_desc: Use image node_id 2 for calling a global function with example usage: extract_alpha_beta_for_device_mesh(alpha_beta_dict, logical_mesh_shape) without return types